The market for driver assistance systems based on millimeter-wave radar sensor technology is gaining momentum. In the near future, the full range of newly introduced car models will be equipped with radar based systems which leads to high volume production with low cost potential. This paper provides background and an overview of the state of the art of millimeter-wave technology for automotive radar applications, including two actual silicon based fully integrated radar chips. Several advanced packaging concepts and antenna systems are presented and discussed in detail. Finally measurement results of the fully integrated radar front ends are shown.

Millimeter-Wave Technology for Automotive Radar Sensors in the 77 GHz Frequency Band

Model Predictive Control for Trajectory Tracking of Unmanned Aerial Vehicles Using ROS -- Design of Fuzzy Logic Controllers to ROS-based UAVs -- Flying Multiple UAVs Using ROS -- SkiROS -- A skill-based robot control architecture on top of ROS -- Control of Mobile Robots using ActionLib -- Parametric Identification of the Dynamics of Mobile Robots and Its Application for the Tuning of Controllers in ROS -- ROSLink: Bridging ROS with the Internet-of-Things for Cloud Robotics -- A ROS Package for Dynamic Bandwidth Management in Multi-Robot Systems -- An autonomous companion UAV for the SpaceBot Cup competition 2015. .

Robot Operating System (ROS) The Complete Reference (Volume 2)

The introduction of photonics in microwave systems is setting new paradigms in radar architectures, providing new features potentially improving the surveillance effectiveness. In particular, photonics is enabling a new generation of the multiband radars able to manage multiple coherent radar signals at different frequencies simultaneously, with high and frequency-independent quality, enabling multispectral imaging for advanced surveillance systems. In fact, thanks to its high stability and huge bandwidth, photonics matches the urgent requirements of the performance and flexibility of the next-generation software-defined radar architectures, and it guarantees system compactness, thanks to the use of a single shared transceiver for multiband operations and to the potentials for photonic integration, which also promises reduced power consumption. In this paper, we present the first field trial, in a maritime scenario, of a fully coherent multiband radar enabled by the use of photonics. The paper reviews the basic concepts exploited for the photonic generation and the detection of the radar signals, and describes the extension to the multiband operation. We present details on the implementation and testing of a dual-band coherent radar system, discussing the potentials for a software-defined radio approach. Moreover, the results obtained after a simple digital data fusion are discussed, highlighting the capability of the coherent photonics-based multiband radars in exploiting the extended observation bandwidth for improving the system detection resolution with minimum computational costs.

Photonics for Radars Operating on Multiple Coherent Bands

In this article, we consider two related aspects of radar resource management, scheduling and task prioritization. Two different methods of scheduling are examined and compared and their differences and similarities highlighted. The comparison suggests that prioritization of tasks plays a dominant role in determining performance. A prioritization scheme based on fuzzy logic is subsequently contrasted and compared with a hard logic approach as a basis for task prioritization. The setting of priorities is shown to be critically dependent on prior expert knowledge. By assessing the priorities of targets and sectors of surveillance according to a set of rules it is attempted to imitate the human decision-making process such that the resource manager can distribute the radar resources in a more effective way. Results suggest that the fuzzy approach is a valid means of evaluating the relative importance of the radar tasks; the resulting priorities have been adapted by the fuzzy logic prioritization method, according to how the radar system perceived the surrounding environment.

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Knowledge-based resource management for multifunction radar: a look at scheduling and task prioritization

This article discusses the benefits and challenges associated with the design of multi-function scalable phased arrays at millimeter wave frequencies. First, applications for phased arrays with tens to hundreds of elements are discussed. Existing solutions for scaling silicon-based phased arrays from microwave to terahertz frequencies are reviewed. The challenges and tradeoffs associated with multiple integration options for W-band phased arrays are analyzed, with special consideration given to packaging and antenna performance. Finally, a solution based on SiGe ICs and organic packages for a 64-element dual-polarized 94 GHz phased array is described, along with associated measurement results.

W-band scalable phased arrays for imaging and communications

Gesture recognition with radar enables remote control of consumer devices such as audio equipment, television sets and gaming consoles. In this paper, experimental results of hand gesture recognition with a low power FMCW radar and a deep convolutional neural network (CNN) are presented. The FMCW radar operates in the 24 GHz ISM frequency band and has an effective isotropic radiated power level of 0 dBm. Since low power consumption is a key aspect for application in consumer devices, the FMCW radar has only one receive channel which is different from other FMCW radars with multiple receive channels that have been described in literature. The recognition of gestures is performed with a deep convolutional neural network that is trained and tested with micro-Doppler spectrograms yielding excellent recognition performance in a simple test case consisting of 3 different gestures. A comparison of the training and test results for an amplitude spectrogram and a complex-valued spectrogram as the CNN input shows that in this test case there is no major benefit of using the phase information in the spectrogram.

Gesture recognition with a low power FMCW radar and a deep convolutional neural network

The new generation of air defence frigates has to be equipped with a high performance sensor suite to cope with the threat in future maritime scenarios. A multifunction radar (MFR) with a single rotating or multiple fixed phased array antennas is often the key element in this sensor suite because it can perform not only surveillance functions but also missile support. Since the MFR must be able to execute multiple functions concurrently, a scheduling mechanism is required that allocates the time and energy resources of the MFR to the radar functions in such a way that the overall performance of the sensor suite is optimized. This paper describes an efficient scheduling algorithm that is able to schedule the dwell requests of each function on the basis of their priority and transmission window. Some results are shown of the scheduling algorithm in a typical scenario in which a number of seaskimmers is engaged by semi-active surface-to-air missiles.

An efficient scheduling algorithm for a multifunction radar

High resolution radar imaging techniques can be used in ballistic missile defence systems to determine the type of ballistic missile during the boost phase (threat typing) and to discriminate different parts of a ballistic missile after the boost phase. The applied radar imaging technique is 2D Inverse Synthetic Aperture Radar (2D-ISAR) in which the Doppler shifts of various parts of the ballistic missile are employed to obtain a high cross-range resolution while the resolution in downrange is achieved with a large radar bandwidth. For a 10 cm downrange resolution, a radar bandwidth of more than 1.5 GHz is required. However, this requirement is not compatible with EM frequency spectrum allocations for long range ballistic missile defence radars that operate in the L, S, and C frequency band. In this paper, a novel coherent multiband ISAR imaging technique is proposed that employs two or more narrowband radar systems that operate in different frequency bands. The coherent multiband imaging process uses an advanced interpolation technique to achieve a very high downrange resolution and produces little artifacts due to noise.

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High resolution radar imaging using coherent multiband processing techniques

Military radar can not only provide kinematic information (position, speed, and course) of land, sea, and air targets during day and night in all weather conditions, but also provides information about the type of target using micro Doppler properties. The micro -Doppler properties of a target are determined by the moving parts on the target body. The number, location, and type of motion of these parts are specific for a given target type and consequently the related micro -Doppler signature can be exploited for classification. Analysis of the micro -Doppler signature may provide detailed properties of rotating parts, such as the rotation rate, number of blades, and blade length. However, for operators and/or image analysts, the interpretation and understanding of radar micro -Doppler spectrograms is much more difficult and time-consuming than the analysis of optical images because of the different nature of the radar imaging principle and target scattering mechanisms. Consequently, there is a need for automatic target recognition (ATR) in radar using micro -Doppler spectrograms.

Deep Learning for Classification of Mini-UAVs Using Micro-Doppler Spectrograms in Cognitive Radar

The objective of a cognitive radar network is to optimise radar performance in the highly variable mission environments that current operational systems encounter, while minimising its interference with other systems and its vulnerability to countermeasures such as jamming and anti-radiation missiles. A cognitive radar network may achieve these challenges by fully exploiting the available radar resources, sharing data among network components and taking into account prior environmental and situational knowledge as well as experience accumulated during operations. This knowledge can vary from high level information such as intelligence about the threat to low level information such as clutter maps. This paper presents a cognitive radar network architecture that supports this functionality and the application of (self)-learning methods. In this paper reinforcement learning is used to maximise the survivability of naval surface ships in a littoral scenario by managing the modes of an air surveillance radar.

Albert Huizing

Papers

Gesture recognition with a low power FMCW radar and a deep convolutional neural network

An efficient scheduling algorithm for a multifunction radar

High resolution radar imaging using coherent multiband processing techniques

Deep Learning for Classification of Mini-UAVs Using Micro-Doppler Spectrograms in Cognitive Radar

A cognitive radar network: Architecture and application to multiplatform radar management